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For businesses who wish to leverage Data Replication solutions for large datasets using Data Lakes, Apache Kafka and Hadoop are two key technologies
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Jo Ramos, David Nelson, and John Van Buren present: Implementing a data lake is a cornerstone of modernizing an enterprise analytics architecture. This session will explore how growing evidence suggests that the difference between successful and unsuccessful data lakes is often determined by whether data integration, data quality and data governance are addressed effectively. The session will use real examples of successful and unsuccessful data lakes to illustrate best practices
Glenn Steffler presents: A data lake offers significant potential for enhancing enterprise analytics and reporting. Learn how IBM’s Data Replication solution can add significant value to your data lake by delivering critical data from source systems to the data lake in real-time
Check out the recording! A data lake offers significant potential for enhancing enterprise analytics and reporting. Learn how IBM’s Data Replication solution can add significant value to your data lake by delivering critical data from source systems to the data lake in real-time
Implementing a data lake is a cornerstone of modernizing an enterprise analytics architecture. This session will explore how growing evidence suggests that the difference between successful and unsuccessful data lakes is often determined by whether data integration, data quality and data governance are addressed effectively. The session will use real examples of successful and unsuccessful data lakes to illustrate best practices
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In this proof of concept, we will be using Cloud Object Storage and storing the data in a bucket, which acts as a data lake. Figure 1 - Cloud Object storage bucket 3
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Paul explained the differences between data lakes and data warehouses, and described that a data lakehouse would be the hypothetical result of a tinder-like union of a data lake and a data warehouse